期刊
IEEE ACCESS
卷 10, 期 -, 页码 40536-40555出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3166901
关键词
Machine learning algorithms; Metaheuristics; Mathematical models; Feature extraction; Whales; Spirals; Linear programming; Artificial intelligence; machine learning; optimization; sine cosine algorithm; modified whale optimization algorithm
资金
- Taif University Researchers Supporting, Taif University, Taif, Saudi Arabia [TURSP-2020/150]
This paper introduces a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA), which aims to solve problems with continuous and binary decision variables. Through testing on various datasets and benchmark functions, the results demonstrate the superior performance of the algorithm in feature selection and engineering design.
This paper proposes a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA to solve problems with continuous and binary decision variables. The SCMWOA algorithm is first tested on nineteen datasets from the UCI Machine Learning Repository with different numbers of attributes, instances, and classes for feature selection. It is then employed to solve several benchmark functions and classical engineering case studies. The SCMWOA algorithm is applied for solving constrained optimization problems. The two tested examples are the welded beam design and the tension/compression spring design. The results emphasize that the SCMWOA algorithm outperforms several comparative optimization algorithms and provides better accuracy compared to other algorithms. The statistical analysis tests, including one-way analysis of variance (ANOVA) and Wilcoxon's rank-sum, confirm that the SCMWOA algorithm performs better.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据